Traffic state prediction models need to capture both spatial and temporal dependencies embedded in traffic data for high prediction accuracy [1]}. Many deep learning models have been developed for this task. One approach is to use convolutional neural network (CNN). As an example, the traffic state of a city-wide network is converted to a grid map which acts as an “image” to be processed by CNN [2]}. Since traffic data have varying structures, researchers have adopted a more nature structure—graph—to represent traffic data (e.g., treat traffic sensors as nodes). This has motivated the use of graph convolutional neural network (GCNN) [3]}. One example is diffusion convolutional recurrent neural Network (DCRNN) [4]}, which combines GCNN with recurrent neural network (RNN) to capture the spatial-temporal dependencies. More recently, graph convolutional gated recurrent neural network (GCGRNN) is proposed to integrate data-driven graph filter and gated recurrent neural network for capturing hidden correlations among traffic sensors without requiring a predefined graph representation [5]}. In this work, we treat GCGRNN and DCRNN, along with two traditional methods (linear regression and historical average), as our target models, and examine their adversarial vulnerability.
